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# Question 1: For all Finance staff members during the month of January, show the distribution of when users logon and logoff by hour using one or more Bar Charts,
# and report the most common login and logoff time for this role.
# Hint: Once you have filtered the data to only Finance staff in January, count the number of logons and logoffs that occur in each hour of the day.
import numpy as np
january = login_data.loc[(login_data["datetime"] >= '2022-01-01 00:00:00') & (login_data["datetime"] <= '2022-02-01 00:00:00')]
finance_and_january = january.loc[january["user"].isin(user_set['Finance'])]
login_and_logoff = np.zeros(24)
login = np.zeros(24)
logoff = np.zeros(24)
for idenx, row in finance_and_january.iterrows():
login_and_logoff[row["datetime"].hour] += 1
if row["action"] == 'login':
login[row["datetime"].hour] += 1
elif row["action"] == 'logoff':
logoff[row["datetime"].hour] += 1
times = []
for i in range(0,24):
times.append(f'{str(i).zfill(2)}:00')
plt.figure(figsize=(10,6))
plt.bar(times, login_and_logoff)
plt.xticks(rotation=90)
plt.title("A plot to show combind log in and off stats - Fig 1")
plt.ylabel("Number of events")
plt.xlabel("Times")
plt.show()
